Seismic data reconstruction is a crucial step in seismic data processing. Traditional methods and deep learning approaches have both been widely used in this field. However, they ignored the interactive learning of inter-channel information, especially in the case of high missing rate where feature extraction became more difficult. To address this issue, we propose an adaptive feature fusion network for the reconstruction of both random and consecutive missing seismic data. The information interaction block is designed into this model to improve the efficiency and adaptability of feature selection. It adaptively emphasizes important feature channels and enables inter-channel information exchange learning. To enhance the ability to capture global and local details, a cross-dimensional feature fusion module is designed at the bottleneck, integrating information from both the channel and spatial dimensions. Additionally, the strategy loss is designed to enable the network to learn the correlations among missing parts of the seismic traces, thereby boosting the reconstruction performance of our model. Compared with other state-of-the-art seismic data reconstruction methods, the proposed algorithm achieves improvements in both qualitative and quantitative evaluations: the reconstruction quality has improved by 20% on both synthetic and field datasets with random missing data. The reconstruction quality has improved by 30% on both synthetic and field datasets with consecutive missing data. At the end of the paper, we conducted ablation experiments, hyperparameter analysis and discussion.

Seismic data reconstruction is a crucial step in seismic data processing. Most existing methods reconstruct seismic data in the spatial domain, often ignoring some important frequency components in the frequency domain, such as high-frequency texture features. Therefore, we propose a frequency-domain self-attention network (FDSANet) to effectively reconstruct seismic data with high-missing-rate. The wavelet transform is employed in this model to better restore weak signals and provide more information at different resolutions. The fast Fourier transform in the frequency-domain self-attention module (FDSAM) enhances global frequency awareness, especially for high-frequency energy. Different frequency components are element-wise multiplied by dynamic weights, effectively suppressing energy leakage and aliasing. Moreover, the nearest-neighbor similarity loss on adjacent shot gathers is incorporated into the loss function to learn information from neighboring shot gathers, further enhancing the reconstruction performance of our model. Experiments on both synthetic and field datasets demonstrate that FDSANet achieves significant improvement over several state-of-the-art methods.

Multi-modal image fusion synthesizes information from multiple sources into a single image, facilitating downstream tasks such as semantic segmentation. Current approaches primarily focus on acquiring informative fusion images at the visual display stratum through intricate mappings. Although some approaches attempt to jointly optimize image fusion and downstream tasks, these efforts often lack direct guidance or interaction, serving only to assist with a predefined fusion loss. To address this, we propose an “Unfolding Attribution Analysis Fusion network” (UAAFusion), using attribution analysis to tailor fused images more effectively for semantic segmentation, enhancing the interaction between the fusion and segmentation. Specifically, we utilize attribution analysis techniques to explore the contributions of semantic regions in the source images to task discrimination. At the same time, our fusion algorithm incorporates more beneficial features from the source images, thereby allowing the segmentation to guide the fusion process. Our method constructs a model-driven unfolding network that uses optimization objectives derived from attribution analysis, with an attribution fusion loss calculated from the current state of the segmentation network. We also develop a new pathway function for attribution analysis, specifically tailored to the fusion tasks in our unfolding network. An attribution attention mechanism is integrated at each network stage, allowing the fusion network to prioritize areas and pixels crucial for high-level recognition tasks. Additionally, to mitigate the information loss in traditional unfolding networks, a memory augmentation module is incorporated into our network to improve the information flow across various network layers. Extensive experiments demonstrate our method’s superiority in image fusion and applicability to semantic segmentation. The code is available athttps://github.com/HaowenBai/UAAFusion.

Residual moveout (RMO) provides critical information for depth-domain tomography. The current industry-standard method for fitting RMO involves scanning high-order polynomial equations. However, this analytical approach does not accurately capture abrupt variation of the RMO, leading to low iteration efficiency in tomographic inversion. Supervised learning-based image segmentation methods for picking can effectively capture local variations; however, they encounter challenges such as a scarcity of reliable training samples and the high complexity of post-processing. To address these issues, this study proposes a deep learning-based cascade picking method. It distinguishes accurate and robust RMOs using a segmentation network and a post-processing technique based on trend regression. Additionally, a data synthesis method is introduced, enabling the segmentation network to be trained on synthetic datasets for effective picking in field data. Furthermore, a set of metrics is proposed to quantify the quality of automatically picked RMOs. Experimental results based on both model and real data demonstrate that, compared to semblance-based methods, our approach achieves greater picking density and accuracy.

Recently, view-based approaches, which recognize a 3D object through its projected 2-D images, have been extensively studied and have achieved considerable success in 3D object recognition. Nevertheless, most of them use a pooling operation to aggregate viewwise features, which usually leads to the visual information loss. To tackle this problem, we propose a novel layer called capsule attention layer (CAL) by using attention mechanism to fuse the features expressed by capsules. In detail, instead of dynamic routing algorithm, we use an attention module to transmit information from the lower level capsules to higher level capsules, which obviously improves the speed of capsule networks. In particular, the view pooling layer of multiview convolutional neural network (MVCNN) becomes a special case of our CAL when the trainable weights are chosen on some certain values. Furthermore, based on CAL, we propose a capsule attention convolutional neural network (CACNN) for 3D object recognition. Extensive experimental results on three benchmark datasets demonstrate the efficiency of our CACNN and show that it outperforms many state-of-the-art methods.

Source point offset (SPO) plays a crucial role in geophysical prospecting, as it places the source points away from obstacles to facilitate exploration efforts. However, it is time-consuming for manual work to consider various intricate conditions, such as the smoothness of source lines and uniformity of fold distribution. Moreover, existing methods cost much time in optimizing one specific objective, which limits the applicability to diverse construction areas. To address this challenge, this letter leverages the widely adopted transformer architecture as the model and uses REINFORCE to train this model, by formulating the SPO as a combinatorial optimization problem. To enhance communication among candidate nodes in SPO, a graph attention layer extracts distinct information among these nodes. Experimental results on four field datasets demonstrate comparable performance of our method with the conventional method, while providing a reference for deviated geometry design quickly in field test survey.

First-break picking is a pivotal procedure in processing microseismic data for geophysics and resource exploration. Recent advancements in deep learning have catalyzed the evolution of automated methods for identifying first-break. Nevertheless, the complexity of seismic data acquisition and the requirement for detailed, expert-driven labeling often result in outliers and potential mislabeling within manually labeled datasets. These issues can negatively affect the training of neural networks, necessitating algorithms that handle outliers or mislabeled data effectively. We introduce the Simultaneous Picking and Refinement (SPR) algorithm, designed to handle datasets plagued by outlier samples or even noisy labels. Unlike conventional approaches that regard manual picks as ground truth, our method treats the true first-break as a latent variable within a probabilistic model that includes a first-break labeling prior. SPR aims to uncover this variable, enabling dynamic adjustments and improved accuracy across the dataset. This strategy mitigates the impact of outliers or inaccuracies in manual labels. Intra-site picking experiments and cross-site generalization experiments on publicly available data confirm our method’s performance in identifying first-break and its generalization across different sites. Additionally, our investigations into noisy signals and labels underscore SPR’s resilience to both types of noise and its capability to refine misaligned manual annotations. Moreover, the flexibility of SPR, not being limited to any single network architecture, enhances its adaptability across various deep learning-based picking methods. Focusing on learning from data that may contain outliers or partial inaccuracies, SPR provides a robust solution to some of the principal obstacles in automatic first-break picking.

In seismic exploration, identifying the first break (FB) is critical in establishing subsurface velocity models. Various automatic picking techniques based on deep learning have been developed to expedite this procedure. The most popular method involves using semantic segmentation networks to pick on a shot gather known as 2-D picking. Concretely, segmentation-based methods input a gather, and produce a binary segmentation map, where the highest value in each column represents the FB. However, currently designed segmentation networks make it difficult to ensure the horizontal continuity of the segmentation. FB jumps also exist in some areas, and it is challenging for current models to detect such jumps. Therefore, it is important to pick as much as possible and ensure horizontal continuity. To address this issue, we propose a novel network for 2-D seismic FB picking. We introduce the dynamic snake convolution (DSConv) from computer vision into U-Net and refer to the new model as dynamic snake U-Net (DSU-Net). Specifically, we develop the original DSConv and propose a novel DSConv module, which can extract the horizontally continuous texture in the shallow features of the shot gather. Many experiments show that DSU-Net demonstrates higher accuracy and robustness than the other 2-D segmentation-based models, achieving state-of-the-art (SOTA) performance in 2-D seismic field surveys. Particularly, it can effectively detect FB jumps and better ensure the horizontal continuity of FBs. In addition, the ablation experiment and the anti-noise experiment, respectively, verify the optimal structure of the DSConv module and the robustness of the picking.

In seismic exploration, first break (FB) picking is a crucial aspect in determining subsurface velocity models, significantly influencing the placement of wells. Many deep neural networks (DNNs)-based automatic picking methods have been proposed to accelerate this process. Significantly, the segmentation-based DNN methods provide a segmentation map and then estimate FB from the map using a thresholding technique. However, these automatic methods applied in field datasets cannot ensure robustness, especially in the case of a low signal-to-noise ratio (SNR). In this article, we introduce uncertainty quantification (UQ) into FB picking and propose a novel uncertainty-based picking deep learning network (UPNet). UPNet specifically consists of two DNNs. A Bayesian network infers a posterior distribution by sampling the segmentation map of FB. Subsequently, a regression network integrates the segmentation map, the original trace, and the low-frequency (LF) trace to infer the FB trace by trace. Finally, a decision-making method provides the final FB based on uncertainty analysis, offering robust FB. Importantly, UPNet avoids post-processing to obtain FB using the threshold method, as in the segmentation-based picking methods, and instead provides the FB of each trace end-to-end. Moreover, UPNet not only estimates the uncertainty of the network output but can also filter out predictions with low confidence. Many experiments have shown that UPNet demonstrates higher accuracy and robustness than the deterministic DNN-based model, achieving state-of-the-art (SOTA) performance in field surveys. In addition, we verify that the calculated uncertainty is significant, which can serve as a reference for human decision-making.

Accurate interpolation of seismic data is crucial for improving the quality of imaging and interpretation. In recent years, deep learning models such as U-Net and generative adversarial networks (GANs) have been widely applied to seismic data interpolation. However, they often underperform when the training and test missing patterns do not match. To alleviate this issue, here we propose a novel framework that is built upon the multimodal adaptable diffusion models. In the training phase, following the common wisdom, we use the denoising diffusion probabilistic model with a cosine noise schedule. This cosine global noise configuration improves the use of seismic data by reducing the involvement of excessive noise stages. In the inference phase, we introduce the denoising diffusion implicit model (DDIM) to reduce the number of sampling steps. Different from the conventional unconditional generation, we incorporate the known trace information into each reverse sampling step for achieving conditional interpolation. To enhance the coherence and continuity between the revealed traces and the missing traces, we further propose two strategies, including successive coherence correction and resampling. Coherence correction penalizes the mismatches in the revealed traces, while resampling conducts cyclic interpolation between adjacent reverse steps. Extensive experiments on synthetic and field seismic data validate our model’s superiority and demonstrate its generalization capability to various missing patterns and different noise levels with just one training session. In addition, uncertainty quantification and ablation studies are also investigated.

Missing traces reconstruction is a key step for seismic data processing. In recent years, researchers have proposed various interpolation methods for seismic trace reconstruction. However, their models are hard to recover the weak signals in the consecutively missing case. Moreover, convolution operation used in these models is not sensitive to long-term dependencies and global information, which affects the reconstruction of the middle part of the missing area. To solve these problems, we propose a wavelet-based swin residual network (WSRN) for seismic data reconstruction. The swin residual block is designed into the U-net framework to improve the local and non-local modeling ability. Furthermore, by replacing the normal sampling layer, the multi-level wavelet transform is introduced to enhance the recovery ability of weak signals, and data augmentation strategy and a hybrid loss function are used to improve the reconstruction performance of WSRN. Experimental results on synthetic and field datasets illustrate that WSRN achieves significant improvement over some representative deep learning methods.

Random noise attenuation is essential in seismic data processing. In this paper, we propose an unsupervised method called “shot2shot with re-de-noising regularization” to remove random noise. Shot2Shot (S2S) is a new way to train a denoising neural network. S2S takes a shot-gather and its multiple neighboring shot-gathers as input and labels of the neural network, respectively. The principle that S2S can eliminate noise is the correlation of seismic waves and the independence of random noise between neighboring shot-gathers. Because neural networks are more likely to learn correlated information between inputs and labels rather than independent information. Although S2S is effective in denoising, this mode of training may lead to relatively coarse results. Therefore, we propose re-de-noising regularization to make the results of S2S more refined. The re-de-noising regularization consists of two penalty terms that balance each other, the re-de-noising term and the stability term. The stability term is responsible for introducing more fine content from the observations, such as weak waves, but this can introduce new noise. Thus the re-de-noising term is used to avoid the interference of this new noise. Experimentally, our method outperforms other state-of-the-art methods in terms of quantitative results. Visually, our method not only removes the noise but also reconstructs the noisy data more completely. In addition, we explain the role of S2S and re-de-noising regularization more intuitively through ablation experiments. Finally, the robustness of the key hyperparameters is discussed.

Seismic data interpolation is an indispensable part of seismic data processing. In recent years, deep-learning-based interpolation algorithms for seismic data have become popular due to their high accuracy. However, a considerable amount of work has focused on the migration of concepts and algorithms in deep-learning-based methods while ignoring the implicit properties of seismic data itself. In this article, we propose the regeneration prior, which is an implicit property of seismic data with respect to the interpolation function, and are used for self-supervised seismic data interpolation tasks. In mathematical form, the regeneration prior can be considered as a regular term describing the structure of the seismic data. Theoretically, the regeneration prior is a necessary condition to obtain an optimal interpolation function. Experimentally, the proposed method achieves significant improvement in accuracy and intuitive visualization in comparison with advanced unsupervised or self-supervised methods. In addition, we provide an intuitive interpretation of the regeneration prior, and our study shows that the regeneration prior plays an anti-overfitting structuring role in the parameter learning process of the interpolation function. Finally, we analyze the robustness of the regeneration prior. The experimental results show that the performance of the regeneration prior is stable despite the fact that the hyperparameters associated with the regeneration prior are perturbed in a considerable range.

Seismic data reconstruction is an important technology in seismic data processing. Existing reconstruction methods have achieved promising performance for regularly/randomly missing cases. However, recovering consecutive missing data remains challenging due to the loss of large amounts of information in local regions. In this paper, we devise a novel network called RRMFI-Net, which is mainly constructed by a Recurrent Residual Multiscale Feature Inference (RRMFI) module and a Recurrence Adjustment Attention (RAA) module. The RRMFI module infers and fills the missing regions multiple times, and uses the result as a clue for the next inference, which makes the result more elegant. To ensure that there is no ambiguity between the results of multiple inferences, we devise an RRA module, which is fused into the RRMFI module to obtain padding information from a long distance. Experimentally, we compare RRMFI-Net with supervised state-of-the-art methods, demonstrating that RRMFI-Net is more effective on multiple indicators. Furthermore, we conduct ablation studies discussing the impact of key network hyperparameters.

Infrared and visible image fusion (IVIF) expects to obtain images that retain thermal radiation information from infrared images and texture details from visible images. In this paper, a model-based convolutional neural network (CNN) model, referred to as Algorithm Unrolling Image Fusion (AUIF), is proposed to overcome the shortcomings of traditional CNN-based IVIF models. The proposed AUIF model starts with the iterative formulas of two traditional optimization models, which are established to accomplish two-scale decomposition, i.e., separating low-frequency base information and high-frequency detail information from source images. Then the algorithm unrolling is implemented where each iteration is mapped to a CNN layer and each optimization model is transformed into a trainable neural network. Compared with the general network architectures, the proposed framework combines the model-based prior information and is designed more reasonably. After the unrolling operation, our model contains two decomposers (encoders) and an additional reconstructor (decoder). In the training phase, this network is trained to reconstruct the input image. While in the test phase, the base (or detail) decomposed feature maps of infrared/visible images are merged respectively by an extra fusion layer, and then the decoder outputs the fusion image. Qualitative and quantitative comparisons demonstrate the superiority of our model, which can robustly generate fusion images containing highlight targets and legible details, exceeding the state-of-the-art methods. Furthermore, our network has fewer weights and faster speed.

Velocity picking, a critical step in seismic data processing, has been studied for decades. Although manual picking can produce accurate normal moveout (NMO) velocities from the velocity spectra of prestack gathers, it is time-consuming and becomes infeasible with the emergence of a large amount of seismic data. Numerous automatic velocity picking methods have thus been developed. In recent years, deep learning (DL) methods have produced good results on the seismic data with medium and high signal-to-noise ratios (SNR). Unfortunately, it still lacks a picking method to automatically generate accurate velocities in situations of low SNR. In this paper, we propose a multi-information fusion network (MIFN) to estimate stacking velocity from the fusion information of velocity spectra and stack gather segments (SGS). In particular, we transform the velocity picking problem into a semantic segmentation problem based on the velocity spectrum images. Meanwhile, the information provided by SGS is used as a prior in the network to assist segmentation. The experimental results on two field datasets show that the picking results of MIFN are stable and accurate for the scenarios with medium and high SNR, and it also performs well in low SNR scenarios. Code is made publicly available at https://github.com/newbee-ML/MIFN-Velocity-Picking.

Seismic data are generally sampled irregularly and sparsely along spatial coordinates because economic costs and obstacles hinder the regular arrangement of geophones in the field. Thus, the sampled seismic data often contain missing traces which result in difficulties for later processing steps. To alleviate this issue, versatile interpolation methods have been developed to interpolate the missing traces. However, the existing models for recovering seismic data with consecutively missing traces in a large amplitude range tend to produce artifacts and blurred signal details. We propose in this paper a hybrid loss guided coarse-to-fine model which consists of a coarse network and a refinement network to allow different regions of seismic data to be recovered in different stages. The coarse network is designed to reconstruct the strong signals and the refinement network is implemented subsequently to recover the weak signals. In addition, the refinement network focuses its attention on the areas which are not well recovered by the coarse network via a weight-masked mechanism. By resorting to the hybrid loss function L1+SSIM+Relativistic Average Least-Square Generative Adversarial Network (RaLSGAN), our model enables more accurate and realistic signal details to be reconstructed. Experiments with synthetic and field data demonstrate that our model is superior to the existing mainstream approaches and the role of the key components is also investigated through ablation studies.

Pan-sharpening is an important technique for remote sensing imaging systems to obtain high resolution multi-spectral images. Recently, deep learning has become the most popular tool for pan-sharpening. This paper develops a model-based deep pan-sharpening approach. Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the generative models for panchromatic images and low resolution multispectral images. Then, the two problems are solved by a gradient projection algorithm, and the iterative steps are generalized into two network blocks. By alternatively stacking the two blocks, a novel network, called gradient projection based pan-sharpening neural network, is constructed. The experimental results on different kinds of satellite datasets demonstrate that the new network out-performs state-of-the-art methods both visually and quantitatively. The codes are available at https://github.com/xsxjtu/GPPNN.

Pansharpening is a widely used image enhancement technique for remote sensing. Its principle is to fuse the input high-resolution single-channel panchromatic (PAN) image and low-resolution multi-spectral image and to obtain a high-resolution multi-spectral (HRMS) image. The existing deep learning pansharpening method has two shortcomings. First, features of two input images need to be concatenated along the channel dimension to reconstruct the HRMS image, which makes the importance of PAN images not prominent, and also leads to high computational cost. Second, the implicit information of features is difficult to extract through the manually designed loss function. To this end, we propose a generative adversarial network via the fast guided filter (FGF) for pansharpening. In generator, traditional channel concatenation is replaced by FGF to better retain the spatial information while reducing the number of parameters. Meanwhile, the fusion objects can be highlighted by the spatial attention module. In addition, the latent information of features can be preserved effectively through adversarial training. Numerous experiments illustrate that our network generates high-quality HRMS images that can surpass existing methods, and with fewer parameters.

Pansharpening is a fundamental issue in remote sensing field. This paper proposes a side information partially guided convolutional sparse coding (SCSC) model for pansharpening. The key idea is to split the low resolution multispectral image into a panchromatic image related feature map and a panchromatic image irrelated feature map, where the former one is regularized by the side information from panchromatic images. With the principle of algorithm unrolling techniques, the proposed model is generalized as a deep neural network, called as SCSC pansharpening neural network (SCSC-PNN). Compared with 13 classic and state-of-the-art methods on three satellites, the numerical experiments show that SCSC-PNN is superior to others. The codes are available at https://github.com/xsxjtu/SCSC-PNN.

The deep learning method, especially convolution neural networks (CNNs), has recently made ground-breaking advances on object detection in very-high-resolution (VHR) optical remote sensing images. However, as CNN is originally designed for the classification of natural images, these methods are not very suitable for object detection of remote sensing images. First, current CNN-based approaches have difficulty to deal with objects that have large rotation variation, which is widely existed in optical remote sensing images. Second, the detectors based on CNN only have limited receptive fields and, thus, can hardly utilize the global contextual information that is essential for accurate detection of small targets. To address these two issues, this article proposes a novel deep learning-based object detection framework, including a geometric transform module (GTM) and a global contextual feature fusion module (GCFM). Especially, the GTM combines rotation and flip transformation to deal with the multiangle characteristics of objects. The GCFM uses a spatial attention mechanism to adaptively involve global contextual information in feature maps to improve the recognition and location accuracy of targets. We introduce the two modules into the YOLOv3 framework to achieve end-to-end detection with high performance and efficiency. Comprehensive evaluations on three publicly available object detection data sets demonstrate the excellent performance of the proposed methods.

Infrared and visible image fusion, a hot topic in the field of image processing, aims at obtaining fused images keeping the advantages of source images. This paper proposes a novel auto-encoder (AE) based fusion network. The core idea is that the encoder decomposes an image into background and detail feature maps with low- and high-frequency information, respectively, and that the decoder recovers the original image. To this end, the loss function makes the background/detail feature maps of source images similar/dissimilar. In the test phase, background and detail feature maps are respectively merged via a fusion module, and the fused image is recovered by the decoder. Qualitative and quantitative results illustrate that our method can generate fusion images containing highlighted targets and abundant detail texture information with strong reproducibility and meanwhile surpass state-of-the-art (SOTA) approaches.

The low-rank tensor factorization (LRTF) technique has received increasing popularity in data science, especially in computer vision applications. Many robust LRTF models have been presented recently. However, none of them take the skewness of data into account. This letter proposes a novel LRTF model for skew data analysis by modeling noise as a Mixture of Asymmetric Laplacians (MoAL). The numerical experiments show that the new model MoAL-LRTF outperforms several state-of-the-art counterparts. The codes for all the experiments are available at https://xsxjtu.github.io/Projects/MoAL/main.html.

The fusion of hyperspectral image (HSI) and multispectral image (MSI) is one of the most significant topics in remote sensing image processing. Recently, deep learning (DL) has emerged as an important tool for this task. However, existing DL-based methods have two drawbacks, that is, limited ability for feature extraction and suffering from spectral distortion. To address these issues, this article presents a novel neural network, where sophisticated techniques are employed, including network-in-network convolutional unit, batch normalization, and skip connection. To make full use of the MSI, the proposed model fuses HSI and MSI at different scales. Besides, this article presents a new loss function, called RMSE, angle and Laplacian (RAP) loss (the combination of the relative mean squared error, angle loss, and Laplacian loss), to deal with both spatial and spectral distortions. Experiments conducted on four data sets have verified the rationality of network structure and the proposed loss function and demonstrated that the proposed novel model outperforms state-of-the-art counterparts.

In the literature of object detection in optical remote sensing images, a popular pipeline is first modifying an off-the-shelf deep neural network, then initializing the modified network by pretrained weights on a source data set, and finally fine-tuning the network on a target data set. The procedure works well in practice but might not make full use of underlying knowledge implied by pretrained weights. In this article, we propose a novel method, referred to as Fisher regularization, for efficient knowledge transferring. Based on Bayes' theorem, the method stores underlying knowledge into a Fisher information matrix and fine-tunes parameters based on the knowledge. The proposed method would not introduce extra parameters and is less sensitive to hyperparameters than classical weight decay. Experiments on NWPUVHR-10 and DOTA data sets show that the proposed method is effective and works well with different object detectors.

In network science and the data mining field, a long-lasting and significant task is to predict the propagation capability of nodes in a complex network. Recently, an increasing number of unsupervised learning algorithms, such as the prominent PageRank (PR) and LeaderRank (LR), have been developed to address this issue. However, in degree uncorrelated networks, this paper finds that PR and LR are actually proportional to in-degree of nodes. As a result, the two algorithms fail to accurately predict the nodes' propagation capability. To overcome the arising drawback, this paper proposes a new iterative algorithm called SpectralRank (SR), in which the nodes' propagation capability is assumed to be proportional to the amount of its neighbors after adding a ground node to the network. Moreover, a weighted SR algorithm is also proposed to further involve a priori information of a node itself. A probabilistic framework is established, which is provided as the theoretical foundation of the proposed algorithms. Simulations of the susceptible-infected-removed model on 32 networks, including directed, undirected, and binary ones, reveal the advantages of the SR-family methods (i.e., weighted and unweighted SR) over PR and LR. When compared with other 11 well-known algorithms, the indices in the SR-family always outperform the others. Therefore, the proposed measures provide new insights on the prediction of the nodes' propagation capability and have great implications in the control of spreading behaviors in complex networks.